Especially from the immunological perspective, the complex interaction of tumor and microenvironment stands to gain significant insight from the use of high-throughput technology. In such studies, multiple tissue types comprising the tumor and its sorted and unsorted infiltrating lymphocyte populations can be stratified by prognostic effect to highlight biological markers with strong translational promise. A limiting factor for jointly immunologic and genomic investigations is the availability of statistical methods to parse evidence for tissue- specific interactions in signal transduction systems and tissue-specific effect modification. To this end, we have undertaken a genomic study involving tumor, tumor-infiltrating lymphocytes, and tumor-associated lymphocytes and we have developed the preliminary methods to identify functional and prognostic ligand- receptor signaling as well as immune-regulatory transcripts. The purpose of this grant is to further develop these methods and to apply them to our study data. Our primary motivation comes from collaborations with ovarian cancer immunologists who seek to use high-throughput gene expression data to hone immunotherapies: our conclusions will be directly relevant to improving the effect of cancer vaccines or removing barriers to their efficacy our methodological results will be applicable beyond this study and will enable future studies of an immunological and genomic nature. As a career development award, this proposal will provide Dr. Eng with the mentorship and protected time required to develop lines of statistical research with the eventual goal to seek the R01 funding required to establish an independent research program in ovarian cancer informatics.